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Description: Speech Recognition - Numbers 1 to 5
Energy normalization and time alignment
References:
[1] L. Rabiner and B.H. Juang,Fundamentals of Speech Recognition, Prentice-Hall, 1993.
％ [2] P.E. Papamichalis, Practical Approaches to Speech Coding, Prentice-Hall, 1987.
％ [3] J.D. Markel and A.H. Gray,Linear Prediction of Speech-Speech Recognition-Numbers 1 to 5 Energy n ormalization and time alignment References : [1] L. Paras and B. H. Juang, Fundamentals of Speech Recognition. Prentice-Hall, 1993. % [2] P. E. Papamichalis, Practical Approaches to Speech Coding, Prentice-Hall, 1987. % [3] J. D. Markel and A. H. Gray, Linear Prediction of Speech
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Size: 1448 |
Author: pan |
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Description: Speech Recognition - Numbers 1 to 5
Energy normalization and time alignment
References:
[1] L. Rabiner and B.H. Juang,Fundamentals of Speech Recognition, Prentice-Hall, 1993.
％ [2] P.E. Papamichalis, Practical Approaches to Speech Coding, Prentice-Hall, 1987.
％ [3] J.D. Markel and A.H. Gray,Linear Prediction of Speech-Speech Recognition-Numbers 1 to 5 Energy n ormalization and time alignment References : [1] L. Paras and B. H. Juang, Fundamentals of Speech Recognition. Prentice-Hall, 1993. % [2] P. E. Papamichalis, Practical Approaches to Speech Coding, Prentice-Hall, 1987. % [3] J. D. Markel and A. H. Gray, Linear Prediction of Speech
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Size: 2203 |
Author: pan |
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Description: This code implements in C++ a basic left-right hidden Markov model
and corresponding Baum-Welch (ML) training algorithm. It is meant as
an example of the HMM algorithms described by L.Rabiner (1) and
others. Serious students are directed to the sources listed below for
a theoretical description of the algorithm. KF Lee (2) offers an
especially good tutorial of how to build a speech recognition system
using hidden Markov models.
Platform: |
Size: 15630 |
Author: aaaaaaa |
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Description: Hidden_Markov_model_for_automatic_speech_recognition
This code implements in C++ a basic left-right hidden Markov model
and corresponding Baum-Welch (ML) training algorithm. It is meant as
an example of the HMM algorithms described by L.Rabiner (1) and
others. Serious students are directed to the sources listed below for
a theoretical description of the algorithm. KF Lee (2) offers an
especially good tutorial of how to build a speech recognition system
using hidden Markov models.
Platform: |
Size: 23484 |
Author: 张志 |
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Description: 计算所汉语词法分析系统ICTCLAS.分词正确率高达97.58%(973专家组评测)，未登录词识别召回率均高于90%，其中中国人名的识别召回率接近98%处理速度为31.5Kbytes/s。ICTCLAS的特色还在于：可以根据需要输出多个高概率结果，有多种输出格式，支持北大词性标注集，973专家组给出的词性标注集合。-Calculate the Chinese Lexical Analysis System ICTCLAS. Segmentation correct rate of 97.58 percent (973 Expert Group on Evaluation), the recall rate of identification of unknown words were higher than 90 percent, of which China s name to identify the recall rate of nearly 98 percent processing speed for 31.5Kbytes/s. Also features ICTCLAS is: can output a number of high probability that there are a variety of output formats, to support the North-of-speech tagging sets, 973 expert group is given a collection of-speech tagging.
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Size: 3140608 |
Author: 站长 |
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Description: Speech Recognition - Numbers 1 to 5
Energy normalization and time alignment
References:
[1] L. Rabiner and B.H. Juang,Fundamentals of Speech Recognition, Prentice-Hall, 1993.
％ [2] P.E. Papamichalis, Practical Approaches to Speech Coding, Prentice-Hall, 1987.
％ [3] J.D. Markel and A.H. Gray,Linear Prediction of Speech-Speech Recognition-Numbers 1 to 5 Energy n ormalization and time alignment References : [1] L. Paras and B. H. Juang, Fundamentals of Speech Recognition. Prentice-Hall, 1993. % [2] P. E. Papamichalis, Practical Approaches to Speech Coding, Prentice-Hall, 1987. % [3] J. D. Markel and A. H. Gray, Linear Prediction of Speech
Platform: |
Size: 1024 |
Author: pan |
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Description: Speech Recognition - Numbers 1 to 5
Energy normalization and time alignment
References:
[1] L. Rabiner and B.H. Juang,Fundamentals of Speech Recognition, Prentice-Hall, 1993.
％ [2] P.E. Papamichalis, Practical Approaches to Speech Coding, Prentice-Hall, 1987.
％ [3] J.D. Markel and A.H. Gray,Linear Prediction of Speech-Speech Recognition-Numbers 1 to 5 Energy n ormalization and time alignment References : [1] L. Paras and B. H. Juang, Fundamentals of Speech Recognition. Prentice-Hall, 1993. % [2] P. E. Papamichalis, Practical Approaches to Speech Coding, Prentice-Hall, 1987. % [3] J. D. Markel and A. H. Gray, Linear Prediction of Speech
Platform: |
Size: 2048 |
Author: pan |
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Description: This code implements in C++ a basic left-right hidden Markov model
and corresponding Baum-Welch (ML) training algorithm. It is meant as
an example of the HMM algorithms described by L.Rabiner (1) and
others. Serious students are directed to the sources listed below for
a theoretical description of the algorithm. KF Lee (2) offers an
especially good tutorial of how to build a speech recognition system
using hidden Markov models.
Platform: |
Size: 15360 |
Author: aaaaaaa |
Hits:
Description: Hidden_Markov_model_for_automatic_speech_recognition
This code implements in C++ a basic left-right hidden Markov model
and corresponding Baum-Welch (ML) training algorithm. It is meant as
an example of the HMM algorithms described by L.Rabiner (1) and
others. Serious students are directed to the sources listed below for
a theoretical description of the algorithm. KF Lee (2) offers an
especially good tutorial of how to build a speech recognition system
using hidden Markov models.
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Size: 23552 |
Author: |
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Description: This function is remove the unvoice section from the voice. Which is end point detection by using rabiner algorithm.
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Size: 2048 |
Author: asi |
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Description: Fundamental of Speech Recognition by Rabiner and Juang.
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Size: 13272064 |
Author: Syed |
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Description: Code to find the speech beginning using rabiner method
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Size: 1024 |
Author: Sakin |
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Description: Code to find speech ending using rabiner method
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Size: 1024 |
Author: Sakin |
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Description: Code essential for rabiner method
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Size: 1024 |
Author: Sakin |
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Description: End point detector
Implementation of "An Algorithm for Determining the Endpoints of Isolated Utterances, L. R. Rabiner and M. R.Sambur
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Size: 2048 |
Author: serdar |
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Description: End point detection"
I implemented an algorithm for detecting start and end point of the word sequence in speech. In this way the Rabiner method, used to perform this work
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Size: 1024 |
Author: mehdi |
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Description: Hidden Markov Model manual book based on Markov Chains equations developed by Rabiner and fellow
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Size: 2182144 |
Author: iwan |
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Description: HMM经典资料。后面还附有语音识别相关资料。
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Size: 2183168 |
Author: Tandy |
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Description: 隐马尔科夫模型与语音识别的开山之作，google scholar中引用次数最多的论文-Hidden Markov model for speech recognition
google scholar most cited papers
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Size: 2183168 |
Author: 圣骑士 |
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Description: 语音信号数字处理(L.R.Rabiner)编写的一本关于语音信号处理的书,很多的都想要的,我今天把它贡献,希望大家喜欢.(Speech signal digital processing (L.R.Rabiner) prepared a book on voice signal processing, many of them want, and I contribute it today, I hope you like it)
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Size: 9340928 |
Author: yiyebaba
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